Fourier Preconditioning for Neural Feature Learning
Mutual information (MI)-inspired feature learning techniques are capable of generating low-dimensional embeddings that retain nonlinear dependence structures, but direct estimations of MI suffer from noisy probability distribution estimates in the low-data regime. The H-Score objective, computed from second-order statistics, provides a practical proxy metric for training feature extraction networks. We prove that H-Score is invariant to invertible transformations in the unrestricted functional setting, but becomes sensitive to input basis rotations under constrained approximation classes. Consequently, we study unitary preconditioning for H-Score networks and show that selecting an approp...
| Authors | Preston Pitzer, Anish Pradhan, Harpreet S. Dhillon |
|---|---|
| Primary category | eess.SP |
| Published | 2 July 2026 |
| Last updated at source | 2 July 2026 |
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